up

tedlium.py

@@ -31,6 +31,9 @@ _DL_URL = "https://huggingface.co/datasets/LIUM/tedlium/resolve/main/"
 
 _LICENSE = "licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)"
 
+_WHISPER_TRANSCRIPT_URL = "https://huggingface.co/datasets/distil-whisper/tedlium/resolve/main/transcription_data/greedy_search/"
+_WHISPER_TRANSCRIPT_URLs = _WHISPER_TRANSCRIPT_URL + "/{split}-transcription.txt"
+
 
 class TedliumReleaseConfig(datasets.BuilderConfig):
     """BuilderConfig for a release of the TED-LIUM dataset."""
@@ -232,6 +235,7 @@ class TedLium(datasets.GeneratorBasedBuilder):
                 "gender": datasets.features.ClassLabel(names=["unknown", "female", "male"]),
                 "file": datasets.Value("string"),
                 "id": datasets.Value("string"),
+                "whisper_transcript": datasets.Value("string"),
             }
         )
         return datasets.DatasetInfo(
@@ -245,20 +249,35 @@ class TedLium(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
+        if self.config.name != "release3":
+            raise ValueError("This dataset is only compatible with the `release3` config.")
+
         archive_path = dl_manager.download(self.config.download_urls)
         # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
         local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+
+        transcription_urls = {split: _WHISPER_TRANSCRIPT_URLs.format(split=split) for split in ["train", "validation", "test"]}
+        transcript_archive_path = dl_manager.download(transcription_urls)
+
         splits = []
         for split, path in self.config.split_paths:
             kwargs = {
                 "filepath": [dl_manager.iter_archive(sharded_path) for sharded_path in archive_path[split]],
                 "local_extracted_archive": local_extracted_archive.get(split),
                 "split_path": path,
+                "whisper_transcript": transcript_archive_path[split if split != "dev" else "validation"]
             }
             splits.append(datasets.SplitGenerator(name=split, gen_kwargs=kwargs))
         return splits
 
-    def _generate_examples(self, filepath, local_extracted_archive, split_path):
+    def _generate_examples(self, filepath, local_extracted_archive, split_path, whisper_transcript):
+        whisper_transcripts = []
+
+        with open(whisper_transcript, encoding="utf-8") as f:
+            for row in f:
+                whisper_transcripts.append(row.rstrip("\n"))
+        idx = 0
+
         """Generate examples from a TED-LIUM stm file."""
         if local_extracted_archive:
             for local_archive in local_extracted_archive:
@@ -289,8 +308,10 @@ class TedLium(datasets.GeneratorBasedBuilder):
                                 "gender": _parse_gender(label),
                                 "file": audio_file,
                                 "id": key,
+                                "whisper_transcript": whisper_transcripts[idx]
                             }
                             yield key, example
+                            idx += 1
 
         else:
             audio_data = {}
@@ -337,14 +358,19 @@ class TedLium(datasets.GeneratorBasedBuilder):
                                 )
                                 audio = {"path": transcript["file"], "array": samples, "sampling_rate": sampling_rate}
                                 key = transcript["id"]
+                                transcript_text = transcript["text"]
+                                whisper_transcription = whisper_transcripts[idx] if transcript_text != "ignore_time_segment_in_scoring" else "ignore_time_segment_in_scoring"
                                 yield key, {
                                     "audio": audio,
-                                    "text": transcript["text"],
+                                    "text": transcript_text,
                                     "speaker_id": transcript["speaker_id"],
                                     "gender": transcript["gender"],
                                     "file": transcript["file"],
                                     "id": transcript["id"],
+                                    "whisper_transcript": whisper_transcription
                                 }
+                                idx += 1
+
                         audio_data = {}
                         transcripts = defaultdict(list)